Deeply seeing through highly turbid water by active polarization imaging

Liu, Fei; Han, Peng; Yi, Wei; Yang, Kui; Huang, Shengzhi; Li, Xuan; Zhang, Guang; Liu, Bai; Shao, Xiaopeng

doi:10.1364/ol.43.004903

Cited by 105 publications

(33 citation statements)

References 12 publications

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“…The light with high DoLP is more likely to generate CP light after scattering [15]. Furthermore, in media consisting of particles comparable in size to the wavelength of natural light, the CP light can survive more multi-scattering events than linearly polarized light [20][21][22][23], which is called the polarization memory effect. Since circularly polarized light is point-symmetric, it does not depend on the scattering angle.…”

Section: Optical Model Of Rainy Conditionsmentioning

confidence: 99%

Advanced Visualization Polarimetric Imaging: Removal of Water Spray Effect Utilizing Circular Polarization

Liu

Han

et al. 2021

Applied Sciences

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Circular polarization (CP) memory is a well-known phenomenon whereby natural light becomes partially circularly polarized after scattering by water spray several times, and the circularly polarized state can be well preserved within a certain propagation distance. In this study, a CP imaging method combined with the multi-scale analysis in the frequency domain is proposed to enhance the vision in rainy conditions. The images were first decomposed into multi-scales. CP characteristics of light were employed in the low-frequency parts to improve the quality of images in rainy conditions, and the high-frequency parts compensated specific structure information. Experimental results demonstrate that the proposed method can remove the water spray effect thereby improving the vision of degraded rainy-day images.

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Section: Optical Model Of Rainy Conditionsmentioning

confidence: 99%

Advanced Visualization Polarimetric Imaging: Removal of Water Spray Effect Utilizing Circular Polarization

Liu

Han

et al. 2021

Applied Sciences

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“…Light attenuation blurs the background of the captured image, and the living organisms and suspended matter found in complex underwater environments may 2 of 16 cause noise pollution and interference [7]. Light from artificial sources used to improve visibility also undergoes scattering, which adversely affects the image quality [8]. These in situ imaging conditions lead to a deterioration of the image, and the plankton features become very tiny.…”

Section: Introductionmentioning

confidence: 99%

Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

Cheng

Ren

Cheng

et al. 2020

Sensors

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In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

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“…Image dehazing is very critical to obtain clear images in hazy weather [1] and scattering underwater environments [2]. Many techniques have been proposed to enhance the quality of hazy images through image processing [3], infrared-visible image fusion [4] and polarimetric imaging [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques have also been combined together, such as polarimetric imaging with image processing [9,10] and polarimetric imaging with image fusion [11], to further improve the performance. These techniques can effectively enhance the contrast of hazy images captured in scattering media, and even increase the imaging distance [2,12]. Among these dehazing techniques, image-processing techniques typically need only one hazy image to do the dehazing process [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Liang

Ren

et al. 2020

Sensors

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Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.

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Deeply seeing through highly turbid water by active polarization imaging

Cited by 105 publications

References 12 publications

Advanced Visualization Polarimetric Imaging: Removal of Water Spray Effect Utilizing Circular Polarization

Advanced Visualization Polarimetric Imaging: Removal of Water Spray Effect Utilizing Circular Polarization

Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

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